DB-GPT/dbgpt/agent/agents/agent.py

from __future__ import annotations

import dataclasses
from typing import Any, Dict, List, Optional, Tuple, Union

from dbgpt.agent.resource.resource_loader import ResourceLoader
from dbgpt.core import LLMClient
from dbgpt.core.interface.llm import ModelMetadata
from dbgpt.util.annotations import PublicAPI

from ..memory.gpts_memory import GptsMemory


class Agent:
    """An interface for AI agent.
    An agent can communicate with other agents and perform actions.
    """

    def __init__(
        self,
        name: str,
        memory: GptsMemory,
        describe: str,
    ):
        """
        Args:
            name (str): name of the agent.
        """
        self._name = name
        self._describe = describe

        # the agent's collective memory
        self._memory = memory

    @property
    def name(self) -> str:
        """Get the name of the agent."""
        return self._name

    @property
    def memory(self) -> GptsMemory:
        return self._memory

    @property
    def describe(self) -> str:
        """Get the name of the agent."""
        return self._describe

    async def a_notification(
        self,
        message: Union[Dict, str],
        recipient: Agent,
        reviewer: Agent,
    ):
        """Notification a message to recipient agent(Receive a record message from the notification and process it according to your own process. You cannot send the message through send and directly return the current final result.)"""

    async def a_send(
        self,
        message: Union[Dict, str],
        recipient: Agent,
        reviewer: Agent,
        request_reply: Optional[bool] = True,
        is_recovery: Optional[bool] = False,
    ) -> None:
        """(Abstract async method) Send a message to recipient agent."""

    async def a_receive(
        self,
        message: Optional[Dict],
        sender: Agent,
        reviewer: Agent,
        request_reply: Optional[bool] = None,
        silent: Optional[bool] = False,
        is_recovery: Optional[bool] = False,
    ) -> None:
        """(Abstract async method) Receive a message from another agent."""

    async def a_review(
        self, message: Union[Dict, str], censored: Agent
    ) -> Tuple[bool, Any]:
        """

        Args:
            message:
            censored:

        Returns:
            bool: whether the message is censored
            Any: the censored message
        """

    async def a_generate_reply(
        self,
        message: Optional[Dict],
        sender: Agent,
        reviewer: Agent,
        silent: Optional[bool] = False,
        rely_messages: Optional[List[Dict]] = None,
        **kwargs,
    ) -> Union[str, Dict, None]:
        """(Abstract async method) Generate a reply based on the received messages.

        Args:
            messages (Optional[Dict]): a dict of messages received from other agents.
            sender: sender of an Agent instance.
        Returns:
            str or dict or None: the generated reply. If None, no reply is generated.
        """

    async def a_reasoning_reply(
        self, messages: Optional[List[Dict]]
    ) -> Union[str, Dict, None]:
        """Based on the requirements of the current agent, reason about the current task goal through LLM
        Args:
            messages:

        Returns:
            str or dict or None: the generated reply. If None, no reply is generated.
        """

    async def a_action_reply(
        self,
        messages: Optional[str],
        sender: Agent,
        **kwargs,
    ) -> Union[str, Dict, None]:
        """
        Parse the inference results for the current target and execute the inference results using the current agent's executor
        Args:
            messages (list[dict]): a list of messages received.
            sender: sender of an Agent instance.
            **kwargs:
        Returns:
             str or dict or None: the agent action reply. If None, no reply is generated.
        """

    async def a_verify_reply(
        self,
        message: Optional[Dict],
        sender: Agent,
        reviewer: Agent,
        **kwargs,
    ) -> Union[str, Dict, None]:
        """
        Verify whether the current execution results meet the target expectations
        Args:
            messages:
            sender:
            **kwargs:

        Returns:

        """

    def reset(self) -> None:
        """(Abstract method) Reset the agent."""


@dataclasses.dataclass
class AgentContext:
    conv_id: str
    gpts_app_name: str = None
    language: str = None
    max_chat_round: Optional[int] = 100
    max_retry_round: Optional[int] = 10
    max_new_tokens: Optional[int] = 1024
    temperature: Optional[float] = 0.5
    allow_format_str_template: Optional[bool] = False

    def to_dict(self) -> Dict[str, Any]:
        return dataclasses.asdict(self)


@dataclasses.dataclass
@PublicAPI(stability="beta")
class AgentGenerateContext:
    """A class to represent the input of a Agent."""

    message: Optional[Dict]
    sender: Agent
    reviewer: Agent
    silent: Optional[bool] = False

    rely_messages: List[Dict] = dataclasses.field(default_factory=list)
    final: Optional[bool] = True

    memory: Optional[GptsMemory] = None
    agent_context: Optional[AgentContext] = None
    resource_loader: Optional[ResourceLoader] = None
    llm_client: Optional[LLMClient] = None

    def to_dict(self) -> Dict:
        return dataclasses.asdict(self)